1. Field of the Invention
This invention relates generally to ophthalmological polarimeter systems for measuring retinal nerve fiber layer (RNFL) retardances and more particularly to an ophthalmological system for detecting the effects of Alzheimer's disease in the human retina.
2. Description of the Related Art
Alzheimer's disease (AD) was first identified and named in 1906 by Dr. Alois Alzheimer, a German neuropathologist. He had been treating a middle-aged female client who presented symptoms of memory loss and disorientation. Five years later the patient died after suffering hallucinations and symptoms of dementia. The manifestations and course of the disease were so unusual that Dr. Alzheimer was unable to classify the disease into any existing category. Postmortem examination of the brain revealed lesions and distortions, including neuritic plaques and neurofibrillary tangles. AD is characterized by severe cognitive impairment that is insidious, progressive, irreversible and eventually fatal. AD accounts for roughly 60–80 percent of all dementia patients in the United States. It proceeds in stages, gradually destroying all cognitive functions. AD generally affects older men and women, with 75 the average age of onset. While the age range for onset is from 52 to 89 years, the disease is also seen (rarely) in younger people. The risk increases with age and the death rate for people with AD is twice that among those of the same age without the disease.
Practitioners in the art have long sought methods for the identification of conditions associated with the early stages of AD to permit early intervention where possible. A definitive diagnosis of Alzheimer's can only be made during an autopsy. The presence of amyloid plaques and neurofibrillary tangles confirm the disease process. In the present art, a probable diagnosis relies on the medical history, physical examination, diagnostic studies and assessment for the presence of delirium and depression following a full mental status evaluation. The observation of signs and symptoms and the ruling out of other disease processes is relied upon for the diagnosis in the absence of pathology reports. The earlier the diagnosis is made, the greater the benefit in managing the clinical course of the illness, which may include measures for protection against head injury or repeated concussions and protection from toxic exposures such as aluminum. Nonsteroidal anti-inflammatory drugs used continuously for more than two years may delay the onset or reduce the likelihood of developing AD, and antioxidants, particularly vitamin E, may reduce oxidative stresses known to contribute to the evolution of AD. But no cure for AD is known in the art and no AD diagnosis can be definitively confirmed without a postmortem autopsy.
The issue of whether the retinal nerve fiber layer (RNFL) is affected in any way by AD is unsettled. Several practitioners report finding the presence of significant central retinal ganglion cell loss to be correlated with the presence of AD, as reported by, for example, Blanks et al. [Blanks J C, Torigoe Y, Hinton D R, Blanks R H, “Retinal Pathology in Alzheimer's Disease I: Ganglion Cell Loss in Foveal/Parafoveal Retina,” Neurobiol. Aging, 17, 377–84 (1996)] and Blanks et al. [Blanks J C, Schmidt S Y, Torigoe Y, Porrello K V, Hinton D R, Blanks R H, “Retinal Pathology in Alzheimer's Disease II: Regional Neuron Loss and Glial Changes in GCL,” Neurobiol. Aging, 17, 385–95 (1996)]. Blanks et al. showed experimental evidence of an overall decrease of 25% in total numbers of neurons in the ganglion cell layer (GCL) of the central retina in AD patients compared to a control sample. Postmortem confirmation of the AD diagnosis was obtained in all cases. Detailed postmortem analyses of GCL neurons at various sites in the foveola showed that the greatest decrease in neuronal density (e.g., 43%) occurred in the central 500 micron foveal region while neuronal losses of 24% to 26% were found further out to 1,500 microns. The temporal region of the central retina was most severely affected with up to 52% loss in neuronal density compared to milder losses in the nasal regions. Close agreement (within 15%) was found between fellow eyes and all neuron sizes were affected similarly in AD patients. The neuron sizes in control retinas decreased with age, a correlation not found in retinas from AD patients.
Several other practitioners, like Blanks et at, have published autopsy studies providing persuasive evidence of the involvement of the retina nerve layer in the AD process, which has induced other practitioners in the art to propose methods for the in vivo examination and analysis of the GCL to identify features related to AD in the earlier stages, which is more precisely denominated Dementia of the Alzheimer's Type (DAT) because it is unconfirmed by autopsy. For example, Hedges et al. [Hedges III T R, Galves R B, Speigelman D, Barbas N R, Peli E, and Yardley C J, “Retinal Nerve Fiber Layer Abnormalities in Alzheimer's Disease,” Acta Ophthalmol. Scand. 1996: 74, 271–75] employed retinal photographs to identify “abnormalities in two groups of living patients; those diagnosed with AD and a control group without an AD diagnosis. Although Hedges et al. find evidence of ganglion cell degeneration related to AD, their method exhibited limited usefulness, especially in advanced cases of AD, because of the difficulty in obtaining and evaluating RNFL photographs. There was some disagreement between observers regarding the quality and frequency of abnormalities that reflected the difficulty in obtaining precise photographs of RNFL features.
Other practitioners report finding no consistent RNFL degeneration when monitoring in vivo DAT patients. For example, Kergoat et al. [Kergoat H, Kergoat M J, Justino L, Robillard A, Bergman H, Chertkow H, “Normal Optic Nerve Head Topography in the Early Stages of Dementia of the Alzheimer Type,” Dement. Geriatr. Cogn. Disord. 2001: 12, 359–63] found no difference between early-stage DAT patients and age-equivalent control subjects when using in vivo measurements of nerve head topography obtained with a Heidelberg retina tomograph. Similarly, in another study, the same practitioners concluded that the RNFL is not altered by the presence of DAT in the early stages according to data obtained from laser polarimetry measurements [Kergoat H, Kergoat M J, Justino L, Chertkow H, Robillard A, Bergman H, “An Evaluation of the Retinal Nerve Fiber Layer Thickness by Scanning Laser Polarimetry in Individuals with Dementia of the Alzheimer Type,”Acta Ophthalmol. Scand. 2001: 79, 187–91]. These in vivo findings from Kergoat et al. are inconsistent with the postmortem findings from several other practitioners, perhaps because of the earlier AD stage or an unidentified source of measurement error. Kergoat et al. examined only the first 15 degrees of the field of view of the fovea.
The scanning laser polarimetry art is described in the commonly-assigned U.S. Pat. Nos. 5,303,709, 5,787,890, 6,112,114, and 6,137,585, all of which are entirely incorporated herein by reference. The scanning laser polarimeter is a diagnostic device that measures the thickness of the RNFL by measuring the retardance of laser light in the RNFL layer and correlating the retardance to RNFL thickness according to well-known principles. The RNFL thickness measurements thus obtained are subject to significant errors arising from (a) uncompensated anterior eye segment birefringence and (b) uncompensated system birefringence in the optical measurement path, including the residual retardance of optical elements. These errors vary unpredictably over the foveal measurement region and tend to mask the RNFL characteristics most useful in identifying the subtle effects of disease processes, such as AD. In particular, two recent improvements have eliminated much of these measurement errors; the anterior segment retardance compensator and the residual retardance canceling system.
The commonly-assigned U.S. Pat. No. 6,356,036 B1, entirely incorporated herein by reference, discloses an improved anterior segment retardance compensator based on an improved polarimetric method for measuring complex (magnitude and axial orientation) birefringence in both the anterior and the posterior segments of the human eye. The anterior segment includes essentially the combined birefringence of the cornea and the crystalline lens, and the posterior segment includes regions at the fundus. The complex birefringence of the anterior segment is first determined so that it can be canceled by a variable retarder to eliminate this source of error in complex posterior segment birefringence measurements. The procedure improves accuracy by using the patient's Henle fiber layer (instead of the lens posterior surface) as a reference surface for determining complex anterior segment birefringence. The above-cited patent application discloses a residual retardance canceling system that eliminates the other important source of measurement error by introducing a method for averaging multiple retardance measurement samples to cancel the effects of residual system birefringence in the diagnostic path. The above-described Kergoat et al. studies apparently did not use either of these two improvements.
Beyond complex retardance measurement error, another problem with attempting to identify the effects on RNFL characteristics related to disease processes such as AD is the evaluation of the RNFL measurement data, which may include a large two-dimensional array of RNFL thickness and topology data, for example. These data must be compared with another similarly large array of data measured for another group of control subjects. Some early practitioners attempted to perform this evaluation simply by studying photographs to ascertain similarities and differences; a process so subjective as to be nearly useless for finding consistent results. Even expert systems for evaluating patterns and relationships in the measurement data arrays cannot alone discover new and unsuspected patterns or relationships in the data. Efforts to generalize expert systems have encountered a number of problems. For example, as the system complexity increases, the system demand for computing resources exceeds available capacity. Expert systems are generally feasible only when narrowly confined and cannot identify new patterns in large data arrays.
Other adaptive systems such as artificial neural networks (ANNs) may be used to discover new and unsuspected patterns or relationships in measured data by first “learning” with “training data” to recognize features and patterns present in the training data before evaluating other data for similar features. ANNs offer a different approach to problem solving and they are sometimes called the sixth generation of computing. They try to provide a tool that both programs itself and learns on its own. ANNs are structured to provide the capability to solve problems without the benefits of an expert and without the need of programming. They can seek patterns in data that no one knows are there.
Another useful adaptive system is the support vector machine (SVM), which is a learning machine that can perform binary classification and regression estimation tasks. The SVM performs structural risk minimization by creating a classifier with minimized Vapnik-Chervonenkis (VC) dimension. If the VC dimension is low, the expected probability of error is low as well, which yields a good generalization.
SVMs non-linearly map their n-dimensional input space into a high-dimensional feature space wherein a linear classifier is constructed. Two results make this approach successful. The generalization ability of the SVM depends only on the VC dimension of the implemented function set and not on the feature space dimensionality. Any function that describes the data well and belongs to a set of low VC dimension can generalize well regardless of the feature space dimension. Construction of the classifier requires only the evaluation of an inner product between two training data vectors, so an explicit (and time-consuming) mapping into the high-dimensional feature space is not necessary. In Hilbert space, for example, inner products have simple kernel representations that can be quickly and easily evaluated. The SVM is well known in the adaptive system art and is described by V. N. Vapnik in a textbook [The Nature of Statistical Learning Theory, Springer-Verlag, N.Y., ISBN 0-387-94559-8, 1995] and in a recent paper [Vapnik, V N, “An Overview of Statistical Learning Theory,” IEEE Trans. Neural Networks, vol. 10, no. 5, 1999]. These adaptive system tools have not been employed to assist with the evaluation of the effects on the RNFL of AD.
There is accordingly a clearly-felt need in the art for a method and system that can measure RNFL features with sufficient accuracy to identify subtle characteristic patterns that are suitable for automated classification to identify the effects of Alzheimer's disease in the earlier in vivo stages. The unresolved problems and deficiencies are clearly felt in the art and are solved by this invention in the manner described below.